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Large language models (LLMs) have transformed numerous AI applications.
The enron corpus: A new dataset for email classification research
Klimt, B. and Yang, Y · 2004
Earlier work this paper cites.
Spatten: Efficient sparse attention architecture with cascade token and head pruning
Wang, H., Zhang, Z., and Han, S · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
Earlier work this paper cites.
Microsoft coco captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L · 2015
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
Earlier work this paper cites.
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Han, S., Mao, H., and Dally, W. J · 2016
Earlier work this paper cites.
Pointer sentinel mixture models, 2016
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Chen, T., Moreau, T., Jiang, Z., Zheng, L., Yan, E., Shen, H., Cowan, M., Wang, L., Hu, Y., Ceze, L., et al · 2018
Earlier work this paper cites.
Pact: Parameterized clipping activation for quantized neural networks
Choi, J., Wang, Z., Venkataramani, S., Chuang, P. I.-J., Srinivasan, V., and Gopalakrishnan, K · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M · 2018
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Vizwiz grand challenge: Answering visual questions from blind people
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., and Bigham, J. P · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2018
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Learned step size quantization
Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., and Modha, D. S · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Hudson, D. A. and Manning, C. D · 2019
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Data-free quantization through weight equalization and bias correction
Nagel, M., Baalen, M. v., Blankevoort, T., and Welling, M · 2019
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Towards vqa models that can read
Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., and Rohrbach, M · 2019
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Triton: an intermediate language and compiler for tiled neural network computations
Tillet, P., Kung, H.-T., and Cox, D · 2019
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HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Wang, K., Liu, Z., Lin, Y., Lin, J., and Han, S · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al · 2020
Cited alongside, same era.
Mcunet: Tiny deep learning on iot devices
Lin, J., Chen, W.-M., Lin, Y., Gan, C., Han, S., et al · 2020
Cited alongside, same era.
Up or down? adaptive rounding for post-training quantization
Nagel, M., Amjad, R. A., Van Baalen, M., Louizos, C., and Blankevoort, T · 2020
Cited alongside, same era.
Program synthesis with large language models, 2021
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., and Sutton, C · 2021
Cited alongside, same era.
Training verifiers to solve math word problems, 2021
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., et al · 2022
Later among the works it cites.
Smoothquant: Accurate and efficient post-training quantization for large language models
Xiao, G., Lin, J., Seznec, M., Demouth, J., and Han, S · 2022
Later among the works it cites.
Zeroquant: Efficient and affordable post-training quantization for large-scale transformers, 2022
Yao, Z., Aminabadi, R. Y., Zhang, M., Wu, X., Li, C., and He, Y · 2022
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Opt: Open pre-trained transformer language models, 2022
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P. S., Sridhar, A., Wang, T., and Zettlemoyer, L · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., and Keutzer, K · 2021
Cited alongside, same era.
Brecq: Pushing the limit of post-training quantization by block reconstruction
Li, Y., Gong, R., Tan, X., Yang, Y., Hu, P., Zhang, Q., Yu, F., Wang, W., and Gu, S · 2021
Cited alongside, same era.
A white paper on neural network quantization
Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., Van Baalen, M., and Blankevoort, T · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Cited alongside, same era.
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
Cited alongside, same era.
Gpt-neox-20b: An open-source autoregressive language model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al · 2022
Cited alongside, same era.
Openflamingo, March 2023
Awadalla, A., Gao, I., Gardner, J., Hessel, J., Hanafy, Y., Zhu, W., Marathe, K., Bitton, Y., Gadre, S., Jitsev, J., Kornblith, S., Koh, P. W., Ilharco, G., Wortsman, M., and Schmidt, L · 2023
Closest in time.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
Closest in time.
Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al · 2023
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TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Feng, S., Hou, B., Jin, H., Lin, W., Shao, J., Lai, R., Ye, Z., Zheng, L., Yu, C. H., Yu, Y., and Chen, T · 2023
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MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., Wu, Y., and Ji, R · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Closest in time.
Grounding language models to images for multimodal generation
Koh, J. Y., Salakhutdinov, R., and Fried, D · 2023
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MLC-LLM, 2023
MLC-Team · 2023
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Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., and Launay, J · 2023
Closest in time.
High-throughput generative inference of large language models with a single gpu
Sheng, Y., Zheng, L., Yuan, B., Li, Z., Ryabinin, M., Fu, D. Y., Xie, Z., Chen, B., Barrett, C., Gonzalez, J. E., et al · 2023
Closest in time.
Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Wei, X., Zhang, Y., Li, Y., Zhang, X., Gong, R., Guo, J., and Liu, X · 2023
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Mm-vet: Evaluating large multimodal models for integrated capabilities
Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L · 2023
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Llama-adapter: Efficient fine-tuning of language models with zero-init attention
Zhang, R., Han, J., Zhou, A., Hu, X., Yan, S., Lu, P., Li, H., Gao, P., and Qiao, Y · 2023
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Mixtral of experts, 2024
Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de las Casas, D., Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T. L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2024
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Vila: On pre-training for visual language models
Lin, J., Yin, H., Ping, W., Lu, Y., Molchanov, P., Tao, A., Mao, H., Kautz, J., Shoeybi, M., and Han, S · 2024
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